code: def parse_args():
      parser = argparse.ArgumentParser(description="Model training")
      # params of training
      parser.add_argument(
          "--config",
          dest="cfg",
          help="The config file.",
          default=None,
          type=str,
      )
      parser.add_argument(
          "--iters",
          dest="iters",
          help="iters for training",
          type=int,
          default=None,
      )
      parser.add_argument(
          "--batch_size",
          dest="batch_size",
          help="Mini batch size of one gpu or cpu",
          type=int,
          default=None,
      )
      parser.add_argument(
          "--learning_rate",
          dest="learning_rate",
          help="Learning rate",
          type=float,
          default=None,
      )
      parser.add_argument(
          "--save_interval",
          dest="save_interval",
          help="How many iters to save a model snapshot once during training.",
          type=int,
          default=1000,
      )
      parser.add_argument(
          "--resume_model",
          dest="resume_model",
          help="The path of resume model",
          type=str,
          default=None,
      )
      parser.add_argument(
          "--save_dir",
          dest="save_dir",
          help="The directory for saving the model snapshot",
          type=str,
          default="./output",
      )
      parser.add_argument(
          "--keep_checkpoint_max",
          dest="keep_checkpoint_max",
          help="Maximum number of checkpoints to save",
          type=int,
          default=5,
      )
      parser.add_argument(
          "--num_workers",
          dest="num_workers",
          help="Num workers for data loader",
          type=int,
          default=0,
      )
      parser.add_argument(
          "--do_eval",
          dest="do_eval",
          help="Eval while training",
          action="store_true",
      )
      parser.add_argument(
          "--log_iters",
          dest="log_iters",
          help="Display logging information at every log_iters",
          default=100,
          type=int,
      )
      parser.add_argument(
          "--use_vdl",
          dest="use_vdl",
          help="Whether to record the data to VisualDL during training",
          action="store_true",
      )
      parser.add_argument(
          "--seed",
          dest="seed",
          help="Set the random seed during training.",
          default=None,
          type=int,
      )
      parser.add_argument(
          "--data_format",
          dest="data_format",
          help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
          type=str,
          default="NCHW",
      )
      parser.add_argument(
          "--profiler_options",
          type=str,
          default=None,
          help="The option of train profiler. If profiler_options is not None, the train "
          "profiler is enabled. Refer to the medseg/utils/train_profiler.py for details.",
      )
      parser.add_argument(
          "--nnunet",
          dest="nnunet",
          help="nnunet training",
          action="store_true",
      )
      parser.add_argument(
          "--precision",
          default="fp32",
          type=str,
          choices=["fp32", "fp16"],
          help="Use AMP (Auto mixed precision) if precision='fp16'. If precision='fp32', the training is normal.",
      )
  
      parser.add_argument(
          "--sw_num", default=None, type=int, help="sw_num"
      )
  
      parser.add_argument(
          "--is_save_data", default=True, type=eval, help="warmup"
      )
  
      parser.add_argument(
          "--has_dataset_json",
          default=True,
          type=eval,
          help="has_dataset_json",
      )
      return parser.parse_args()
  
file_path: PaddleSeg/contrib/MedicalSeg/train.py
imports: import random
  import argparse
  import paddle
  import numpy
  from medicalseg.cvlibs import manager, Config
  from medicalseg.utils import get_sys_env, logger
  from medicalseg.core import train
  import nnunet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser